1. Technical Field
The present disclosure relates generally to an apparatus and method for detecting an object using a multi-directional integral image and, more particularly, to an apparatus and method for detecting an object using a multi-directional integral image, which are capable of reducing the amount of computation for an overlap area using a multi-directional integral image in the calculation of partial integral images required for the detection of an object.
2. Description of the Related Art
In general, In order to detect an object, a machine learning process of learning the corresponding object is required. In the case of the Adaboost algorithm that is chiefly used as a machine learning method for such machine learning, an object is detected using an integral image.
FIGS. 1A and 1B are diagrams illustrating an example of a conventional method of detecting an object using an integral image.
Referring to FIG. 1A, in the conventional method of detecting an object using an integral image, an integral image 103 is obtained from a full image 102 that is formed by obtaining the pixel values of an original image 101 having w*h pixels.
In this case, each of w and h is one of natural numbers, and w may correspond to width and h may correspond to height.
In the integral image 103, a value corresponding to each pixel is a value that is obtained by adding all the values of pixels that are defined by pixels ranging from the corresponding pixel to the leftmost pixel in a lateral direction and pixels ranging from the corresponding pixel to the uppermost pixel in a vertical direction.
Referring to FIG. 1B, in the conventional method of detecting an object using an integral image, an object is detected along a lateral direction 111, a vertical direction 112 and a diagonal direction 113 using an integral image 103 and an object pattern 110 having a size of m*n.
In this case, each of m and n is one of natural numbers, wherein m and n are smaller than w and h, respectively, and m may correspond to the lateral length of the object pattern 110, and n may correspond to the vertical length of the object pattern 110.
FIGS. 2A and 2B are diagrams illustrating an example of the numbers of bits required for the conventional method of detecting an object using an integral image.
FIG. 2A is a full image formed by obtaining the pixel values of an original image, and FIG. 2B is an integral image calculated from FIG. 2A.
In this case, the value of a pixel at the lower right corner of FIG. 2B corresponds to a value obtained by adding the values of all the pixels of the full image of FIG. 2A, and a large number of bits are required to store such a value.
In particular, in the case of FIG. 2B, as the resolution of a full image increases, an exponentially increasing amount of memory is required to calculate a related integral image.
In order to overcome the above problem, an object may be detected using partial integral images.
FIGS. 3A and 3B are diagrams illustrating an example of a conventional method of detecting an object using partial integral images.
Referring to FIG. 3A, in the conventional method of detecting an object using partial integral images, a full image 301 is segmented into areas 311, 312 and 313 having a size of x*y, and integral images 321, 322 and 323 are calculated from the areas 311, 312 and 313, respectively.
In this case, each of x and y is one of natural numbers, wherein x and y are smaller than w and h, respectively, x may correspond to the lateral length of the partial areas 311, 312 and 313, and y may correspond to the vertical length of the partial areas 311, 312 and 313.
Referring to FIG. 3B, in the conventional method of detecting an object using an integral image, an object is detected along a lateral direction 321, a vertical direction 322 and a diagonal direction 323 using integral images 321, 322 and 323 and an object pattern 330 having a size of m*n.
In this case, each of m and n is one of natural numbers, wherein m and n are smaller than x and y, respectively, m may correspond to the lateral length of the object pattern 330, and n may correspond to the vertical length of the object pattern 330.
FIG. 4 is a diagram illustrating an example of an overlap computational area occurring in the conventional method of detecting an object using partial integral images.
Referring to FIG. 4, in the conventional method of detecting an object using partial integral images, when the full image 301 is segmented into the areas 311, 312 and 313 having a predetermined size, an overlap corresponding to the area of the object pattern is made in order to ensure the continuity of object detection, and thus an overlap area 410 occurs.
In this case, the number of overlap areas 410 increases in proportion to the size of the full image, in inverse proportion to the size of the partial images, and in proportion to the size of the area of the object pattern.
For example, when an integral image 322 is generated after an integral image 321 has been generated, overlap areas 421 and 422 occur, and the amount of computation increases as if an integral image is generated once more.
That is, with respect to the integral images 321, 322 and 323, the amount of computation is increased by a value corresponding to the overlap areas 422 and 423.
As a result, when an integral image is calculated using a full image, a problem arises in that an exponentially increasing amount of memory is required. When partial integral images are used in order to overcome the above problem, another problem arises in that the amount of computation increases in order to calculate an overlap area.
U.S. Patent Application Publication No. 2006-0181740 discloses a device and method for eliminating a block artifact phenomenon, and introduces a technology for segmenting an image frame into blocks and then determining the edges thereof.
However, this conventional technology cannot also overcome the problem of an increase in the amount of computation attributable to an overlap area in the use of partial integral images.
Therefore, there is an urgent need for a new object detection technology that is capable of calculating each partial integral image along a specific direction and utilizing an intersection integral image and a multi-directional integral image, thereby reducing the amount of computation for an overlap area.